Groundwater Withdrawal Prediction Using Integrated Multitemporal Remote Sensing Data Sets and Machine Learning

Effective monitoring of groundwater withdrawals is necessary to help mitigate the negative impacts of aquifer depletion. In this study, we develop a holistic approach that combines water balance components with a machine learning model to estimate groundwater withdrawals. We use both multitemporal satellite and modeled data from sensors that measure different components of the water balance and land use at varying spatial and temporal resolutions. These remote sensing products include evapotranspiration, precipitation, and land cover. Due to the inherent complexity of integrating these data sets and subsequently relating them to groundwater withdrawals using physical models, we apply random forests-a state of the art machine learning algorithm-to overcome such limitations. Here, we predict groundwater withdrawals per unit area over a highly monitored portion of the High Plains aquifer in the central United States at 5 km resolution for the Years 2002-2019. Our modeled withdrawals had high accuracy on both training and testing data sets (R-2 approximate to 0.99 and R-2 approximate to 0.93, respectively) during leave-one-out (year) cross validation with low mean absolute error (MAE) approximate to 4.31 mm and root-mean-square error (RMSE) approximate to 13.50 mm for the year 2014. Moreover, we found that even for the extreme drought year of 2012, we have a satisfactory test score (R-2 approximate to 0.84) with MAE approximate to 9.72 mm and RMSE approximate to 24.17 mm. Therefore, the proposed machine learning approach should be applicable to similar regions for proactive water management practices.